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Increased segregation of brain networks in focal epilepsy: An fMRI graph theory finding.

Pedersen M, Omidvarnia AH, Walz JM, Jackson GD - Neuroimage Clin (2015)

Bottom Line: Graph theory represents a powerful quantitative framework for investigation of brain networks.We postulate that network regularity, or segregation of the nodes of the networks, may be an adaptation that inhibits the conversion of the interictal state to seizures.It remains possible that this may be part of the epileptogenic process or an effect of medications.

View Article: PubMed Central - PubMed

Affiliation: The Florey Institute of Neuroscience and Mental Health, Austin Campus, Melbourne, VIC, Australia ; Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC, Australia.

ABSTRACT
Focal epilepsy is conceived of as activating local areas of the brain as well as engaging regional brain networks. Graph theory represents a powerful quantitative framework for investigation of brain networks. Here we investigate whether functional network changes are present in extratemporal focal epilepsy. Task-free functional magnetic resonance imaging data from 15 subjects with extratemporal epilepsy and 26 age and gender matched healthy controls were used for analysis. Local network properties were calculated using local efficiency, clustering coefficient and modularity metrics. Global network properties were assessed with global efficiency and betweenness centrality metrics. Cost-efficiency of the networks at both local and global levels was evaluated by estimating the physical distance between functionally connected nodes, in addition to the overall numbers of connections in the network. Clustering coefficient, local efficiency and modularity were significantly higher in individuals with focal epilepsy than healthy control subjects, while global efficiency and betweenness centrality were not significantly different between the two groups. Local network properties were also highly efficient, at low cost, in focal epilepsy subjects compared to healthy controls. Our results show that functional networks in focal epilepsy are altered in a way that the nodes of the network are more isolated. We postulate that network regularity, or segregation of the nodes of the networks, may be an adaptation that inhibits the conversion of the interictal state to seizures. It remains possible that this may be part of the epileptogenic process or an effect of medications.

No MeSH data available.


Related in: MedlinePlus

Targeted attack of networks. Network nodes of highest LE (A)and GE (B)were removed iteratively for the complex, regular and random network models in Fig.2, and the subsequent number of surviving connections was counted. A high number of lost connections indicate that the network is fault intolerant, i.e.,the curve in the above graph above is ‘left shifted’, or more in the upper left quadrant. The graphs towards the lower right quadrant are more fault tolerant. A)The regular network is robust to local network perturbations (arrow), whereas in B)both regular and random networks are robust to global network perturbations (arrow).
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f0025: Targeted attack of networks. Network nodes of highest LE (A)and GE (B)were removed iteratively for the complex, regular and random network models in Fig.2, and the subsequent number of surviving connections was counted. A high number of lost connections indicate that the network is fault intolerant, i.e.,the curve in the above graph above is ‘left shifted’, or more in the upper left quadrant. The graphs towards the lower right quadrant are more fault tolerant. A)The regular network is robust to local network perturbations (arrow), whereas in B)both regular and random networks are robust to global network perturbations (arrow).

Mentions: To maximise the fault tolerance of the network, i.e.,maintaining overall network functioning despite local network disruption (Dubrova, 2013), an ‘unhealthy’ node in an adaptive network is best to detach itself from the wider network. Simulation data based on our network models outlined in Fig.2 show that a regular network is more robust to local network damage than a complex or random network (see Supplementary Fig.1A). This indicates that regular networks are more fault tolerant. Although the underlying biological mechanisms of our finding of a more regularised network topology in focal epilepsy is unknown, we postulate that a neuromechanistic process of fault tolerance that segregates network nodes may prevent the brain from continuously seizing, and in the event of a seizure, prevent seizure spread. Perilesional cortex contains neurons with aberrant firing patterns, giving them a propensity to seize (Neubauer etal., 2014). Neuronal processes that contain or counteract these abnormal epileptic effects, such as network fault tolerance, may result in ‘isolation’ of perilesional cell structures, thus preventing focal seizure instigation and spread.


Increased segregation of brain networks in focal epilepsy: An fMRI graph theory finding.

Pedersen M, Omidvarnia AH, Walz JM, Jackson GD - Neuroimage Clin (2015)

Targeted attack of networks. Network nodes of highest LE (A)and GE (B)were removed iteratively for the complex, regular and random network models in Fig.2, and the subsequent number of surviving connections was counted. A high number of lost connections indicate that the network is fault intolerant, i.e.,the curve in the above graph above is ‘left shifted’, or more in the upper left quadrant. The graphs towards the lower right quadrant are more fault tolerant. A)The regular network is robust to local network perturbations (arrow), whereas in B)both regular and random networks are robust to global network perturbations (arrow).
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4477107&req=5

f0025: Targeted attack of networks. Network nodes of highest LE (A)and GE (B)were removed iteratively for the complex, regular and random network models in Fig.2, and the subsequent number of surviving connections was counted. A high number of lost connections indicate that the network is fault intolerant, i.e.,the curve in the above graph above is ‘left shifted’, or more in the upper left quadrant. The graphs towards the lower right quadrant are more fault tolerant. A)The regular network is robust to local network perturbations (arrow), whereas in B)both regular and random networks are robust to global network perturbations (arrow).
Mentions: To maximise the fault tolerance of the network, i.e.,maintaining overall network functioning despite local network disruption (Dubrova, 2013), an ‘unhealthy’ node in an adaptive network is best to detach itself from the wider network. Simulation data based on our network models outlined in Fig.2 show that a regular network is more robust to local network damage than a complex or random network (see Supplementary Fig.1A). This indicates that regular networks are more fault tolerant. Although the underlying biological mechanisms of our finding of a more regularised network topology in focal epilepsy is unknown, we postulate that a neuromechanistic process of fault tolerance that segregates network nodes may prevent the brain from continuously seizing, and in the event of a seizure, prevent seizure spread. Perilesional cortex contains neurons with aberrant firing patterns, giving them a propensity to seize (Neubauer etal., 2014). Neuronal processes that contain or counteract these abnormal epileptic effects, such as network fault tolerance, may result in ‘isolation’ of perilesional cell structures, thus preventing focal seizure instigation and spread.

Bottom Line: Graph theory represents a powerful quantitative framework for investigation of brain networks.We postulate that network regularity, or segregation of the nodes of the networks, may be an adaptation that inhibits the conversion of the interictal state to seizures.It remains possible that this may be part of the epileptogenic process or an effect of medications.

View Article: PubMed Central - PubMed

Affiliation: The Florey Institute of Neuroscience and Mental Health, Austin Campus, Melbourne, VIC, Australia ; Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC, Australia.

ABSTRACT
Focal epilepsy is conceived of as activating local areas of the brain as well as engaging regional brain networks. Graph theory represents a powerful quantitative framework for investigation of brain networks. Here we investigate whether functional network changes are present in extratemporal focal epilepsy. Task-free functional magnetic resonance imaging data from 15 subjects with extratemporal epilepsy and 26 age and gender matched healthy controls were used for analysis. Local network properties were calculated using local efficiency, clustering coefficient and modularity metrics. Global network properties were assessed with global efficiency and betweenness centrality metrics. Cost-efficiency of the networks at both local and global levels was evaluated by estimating the physical distance between functionally connected nodes, in addition to the overall numbers of connections in the network. Clustering coefficient, local efficiency and modularity were significantly higher in individuals with focal epilepsy than healthy control subjects, while global efficiency and betweenness centrality were not significantly different between the two groups. Local network properties were also highly efficient, at low cost, in focal epilepsy subjects compared to healthy controls. Our results show that functional networks in focal epilepsy are altered in a way that the nodes of the network are more isolated. We postulate that network regularity, or segregation of the nodes of the networks, may be an adaptation that inhibits the conversion of the interictal state to seizures. It remains possible that this may be part of the epileptogenic process or an effect of medications.

No MeSH data available.


Related in: MedlinePlus